Regress: Add marginal effects for logistic regression.

Pivotal tracker: 51919919

Computes marginal effects for logistic regression using the average
of marginal effects at each sample observation. Supporting stats
include standard errors (calcualted by the delta method), t-stats
and p-values calculated in the same manner as the Linear regression
module.
Regress: Added clustered standard errors for linear regression

Pivotal 51117917

Computes the clustered standard errors for linear regression.
Regress: Design document for Marginal Effects

PivotalTracker: 40469833, 51117917, 51919919

Design document for marginal effects in regression.

Includes the following:
- Computation for marginal effects for logisitic and multilogistic regression
- Computation for standard errors for marginal effects of logistic regression
- Bug fixes for a bunch of errors that prevented proper compilation
of the design doc.
- Moved Clustered Sandwich Estimators within Regression since it is
currently used in the context of regression methods.
Utilities: Correct return behaviour for array_to_string

Previously we didn't have a return value for array_to_string in the
utilities leading to issues in GPDB 4.1. Further, marginal effects
also didn't take care of the special handing required for arrays
passed in GPDB 4.1 from SQL to Python. These issues have been rectified
in this commit.
Regress: Added a section into the design doc descibing robust variance

An overview of the sandwich operator and its applications for linear,
logistic, and multilogistic regression have been added into the design
doc.
Bug fix for logistic
Many small fixes for logstic regression
minor changes from code review
Merge remote-tracking branch 'upstream/logregrInputFix' into logregrInputFix